Senior Machine Learning Engineer

🚨 Machine Learning Engineer (Mid–Senior) – 12-Month Freelance Contract (Hybrid – Brussels)

AI. Real-World Impact. Not a side project.

I’m working with a major European insurer who’s investing heavily in AI to transform how risk is modeled, fraud is detected, and claims are processed.

They’ve launched a new program to push advanced machine learning into core insurance systems, and they’re assembling a team of hands-on ML engineers to make it happen.

This isn’t academic research, it’s applied ML in a highly regulated, data-rich environment.

🔍 The Role

We’re looking for a Machine Learning Engineer to design, build, and deploy models in areas such as:

• Fraud detection across high-volume transactional data
• Risk modeling with structured + unstructured datasets
• Predictive analytics for claims and customer behavior
• NLP-driven automation for documents and claims processing

📍 12-month freelance contract, strong extension potential.

Hybrid in Brussels, on-site 2–3 days/week for collaboration with product + actuarial teams.

🧠 What We’re Looking For

Strong ML engineers who know how to get models into production.

You’ve built and deployed ML in real-world environments, you understand the trade-offs between theory and application, and you can handle end-to-end pipelines.

Must-haves:

• 4+ years in ML engineering
• Strong with Python, PyTorch/TensorFlow, MLOps tooling
• Experience with structured/tabular data, time-series, or NLP
• Solid grasp of optimisation, performance
• Background in CI/CD, Docker/K8s, cloud or hybrid infra
• Able to work onsite in Brussels (hybrid, 2–3 days/week)

🛠️ Tech Environment

• Languages/Frameworks: Python, PyTorch, TensorFlow, Scikit-learn
• MLOps/Infra: MLflow, Airflow, Kubernetes, Docker, Terraform
• Cloud: AWS / GCP hybrid environments
• Data: SQL/NoSQL, insurance domain data (claims, policy, transactional)

🌍 Why This Project?

Insurance is an industry where ML actually changes outcomes, catching fraud before it happens, improving fairness in underwriting, and speeding up claims when customers need it most.

• Applied ML on massive, complex datasets
• Autonomy and ownership from day one
• Collaborate with actuaries, data scientists, and product leads
• Build models that make real business and customer impact